Working student - Machine Learning

You will work on the implementation of applicable machine learning pipelines for existing simulation environments or hardware-related systems as well as on the scientific investigation of sophisticated ML methods and architectures. A particular focus is on unsupervised and semi-supervised learning methods, especially for the analysis, modelling and evaluation of time series data.

Your tasks

• Evaluation and validation of selected machine and deep learning methods using available data sets and benchmarks
• Design, implementation and further development of applicable ML pipelines for time series data
• Integration and deployment of developed solutions in existing simulation environments and/or hardware-related systems
• Scientific analysis and evaluation of advanced ML concepts, methods and architectures with regard to their applicability in the space domain
• Independent familiarisation with new scientific issues and development of relevant literature and methods
• Preparation, documentation and presentation of results
• Collaboration on scientific publications

• Enrolment in a scientific Master’s degree programme, preferably in computer science, mathematics, statistics, data science, aerospace or a comparable scientific and technical degree programme
• Good knowledge of at least one programming language, preferably Python
• Experience in dealing with version control and modern development processes, ideally with Git and CI/CD
• Good written and spoken English skills
• Solid knowledge of statistics as well as machine learning and deep learning
• Ideally initial experience with advanced topics such as federated learning, explainable AI, anomaly detection in time series or self-supervised learning
• Ideally knowledge or practical experience in analysing time series data
• Ideally initial experience in scientific work, for example through seminar papers, project work or theses as well as in writing scientific texts
• Ideally submit code examples, a Git repository or other evidence of practical programming experience with your application

Remuneration will be paid up to pay group 3/5 TVÖD, depending on qualifications and tasks assigned.